Neuromuscular training to strengthen leg muscles is an important part of the treatment of musculoskeletal disorders and chronic diseases and preventing age-related muscle loss. This study evaluates different individualization approaches and their real-time implementation for OpenSim musculoskeletal models to estimate the external knee adduction moment during a leg-press exercise. A robotic neuromuscular training platform was utilized to perform isometric and dynamic leg extension exercises. Data were collected for 13 subjects using a 3D motion capture system and force plate measurements from the robotic training platform. Functional joint parameters, determined through dynamic reference movements, were integrated into the OpenSim models, allowing a personalized representation of the hip, knee, and ankle joints. This integration was compared with a conventional scaling method. The results indicate that the incorporation of functional joint axes can significantly enhance the accuracy of biomechanical simulations. These methods provide a real-time and a more precise estimate of the external knee adduction moment compared to conventional scaling approaches and underscore the importance of individualized model parameters in biomechanical research.

Copyright: © 2025 Goell et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

神经肌肉训练以增强腿部肌肉是治疗骨骼肌肉系统疾病和慢性病以及预防与年龄相关的肌肉流失的重要部分。本研究评估了不同的个性化方法及其在OpenSim骨骼肌肉模型中的实时实现，用于估算在腿压练习中膝关节内收力矩的外部值。利用了一个机器人神经肌肉训练平台来执行等距和动态腿部伸展练习。使用三维运动捕捉系统和来自机器人训练平台的力量板测量数据收集了13名受试者的数据。通过动态参考动作确定的功能关节参数被整合到OpenSim模型中，从而允许髋、膝和踝关节的个性化表示。这种整合与传统的比例方法进行了比较。结果表明，在生物力学模拟中纳入功能关节轴可以显著提高准确性。这些方法提供了比传统比例方法更实时且精确估算外部膝关节内收力矩的方法，并强调了在生物力学研究中个体化模型参数的重要性。

版权：© 2025 Goell等人。本作品采用知识共享署名许可协议发布，该许可协议允许在任何媒介中无限制地使用、分发和复制本文档，前提是保留原始作者和来源的信用。